The adoption of self-driving cars will certainly revolutionize our lives, even though they may take more time to become fully autonomous than initially predicted. The first vehicles are already present in certain cities of the world, as part of experimental robot-taxi services. However, most existing studies focus on the navigation part of such vehicles. We currently miss methods, datasets, and studies to assess the in-cabin human component of the adoption of such technology in real-world conditions. This paper proposes an experimental framework to study the activities of occupants of self-driving cars using a multidisciplinary approach (computer vision associated with human and social sciences), particularly non-driving related activities. The framework is composed of an experimentation scenario, and a data acquisition module. We seek firstly to capture real-world data about the usage of the vehicle in the nearest possible, real-world conditions, and secondly to create a dataset containing in-cabin human activities to foster the development and evaluation of computer vision algorithms. The acquisition module records multiple views of the front seats of the vehicle (Intel RGB-D and GoPro cameras); in addition to survey data about the internal states and attitudes of participants towards this type of vehicle before, during, and after the experimentation. We evaluated the proposed framework with the realization of real-world experimentation with 30 participants (1 hour each) to study the acceptance of SDCs of SAE level 4.

翻译：自动驾驶汽车的普及必将彻底改变我们的生活，尽管其完全自主化所需时间可能比最初预测的更久。首批车辆已在全球部分城市作为实验性机器人出租车服务投入使用。然而，现有研究多聚焦于此类车辆的导航环节，目前尚缺乏用于评估真实场景下该技术应用时座舱内人类因素的方法、数据集及研究。本文提出一个实验框架，采用多学科方法（计算机视觉与人文社会科学相结合），重点研究自动驾驶汽车乘员的活动，特别是非驾驶相关活动。该框架由实验场景和数据采集模块构成。我们的首要目标是尽可能在真实条件下捕获车辆使用数据，其次创建包含座舱内人类活动的数据集，以促进计算机视觉算法的开发与评估。数据采集模块通过Intel RGB-D相机和GoPro相机多角度记录车辆前排座椅画面，同时收集实验前后及过程中参与者对这类车辆的内部状态与态度的调查数据。我们通过开展包含30名参与者（每人1小时）的真实场景实验，评估了所提框架在SAE L4级自动驾驶汽车接受度研究中的有效性。